In this article we investigate the problem
of human action recognition in static images. By ac-
tion recognition we intend a class of problems which
includes both action classication and action detection
(i.e. simultaneous localization and classication). Bag-
of-words image representations yield promising results
for action classication, and deformable part models
perform very well object detection. The representations
for action recognition typically use only shape cues and
ignore color information. Inspired by the recent success
of color in image classication and object detection, we
investigate the potential of color for action classication
and detection in static images.
We perform a comprehensive evaluation of color de-
scriptors and fusion approaches for action recognition.
Experiments were conducted on the three datasets most
used for benchmarking action recognition in still im-
ages: Willow, PASCAL VOC 2010 and Stanford-40.
Our experiments demonstrate that incorporating color
information considerably improves recognition perfor-
mance, and that a descriptor based on color names
outperforms pure color descriptors. Our experiments
demonstrate that late fusion of color and shape infor-
mation outperforms other approaches on action recog-
nition. Finally, we show that the dierent color-shape
fusion approaches result in complementary information
and combining them yields state-of-the-art performance
for action classication.

Images and movies

BibTex references

@Article\{SRV2013,
author = "Fahad Shahbaz Khan and Muhammad Anwer Rao and Joost van de Weijer and Andrew D. Bagdanov and Antonio Lopez and Michael Felsberg",
title = "Coloring Action Recognition in Still Images",
journal = "International Journal of Computer Vision (IJCV)",
number = "3",
volume = "105",
pages = "205--221",
month = "dec",
year = "2013",
abstract = "In this article we investigate the problem
of human action recognition in static images. By ac-
tion recognition we intend a class of problems which
includes both action classication and action detection
(i.e. simultaneous localization and classication). Bag-
of-words image representations yield promising results
for action classication, and deformable part models
perform very well object detection. The representations
for action recognition typically use only shape cues and
ignore color information. Inspired by the recent success
of color in image classication and object detection, we
investigate the potential of color for action classication
and detection in static images.
We perform a comprehensive evaluation of color de-
scriptors and fusion approaches for action recognition.
Experiments were conducted on the three datasets most
used for benchmarking action recognition in still im-
ages: Willow, PASCAL VOC 2010 and Stanford-40.
Our experiments demonstrate that incorporating color
information considerably improves recognition perfor-
mance, and that a descriptor based on color names
outperforms pure color descriptors. Our experiments
demonstrate that late fusion of color and shape infor-
mation outperforms other approaches on action recog-
nition. Finally, we show that the dierent color-shape
fusion approaches result in complementary information
and combining them yields state-of-the-art performance
for action classication.",
ifactor = "3.533",
quartile = "1",
area = "COMPUTER SCIENCE, ARTIFICIAL I",
url = "http://cic.uab.cat/Public/Publications/2013/SRV2013"
}